The msm package in R provides a flexible and comprehensive tool for fitting Markov multi-state models to panel data. These models are used to analyze the progression of diseases or other processes over time, where individuals move between different states. The package allows for the inclusion of covariates, time-varying intensities, and hidden Markov models for misclassified or noisy data. It is designed to be user-friendly, with detailed documentation and support for various types of data, including chronic disease progression and screening. The package supports both time-homogeneous and time-inhomogeneous models, where transition intensities may vary over time. It also handles censored states, where the exact state at the end of the study is unknown. Hidden Markov models are used when the true states are not directly observed but inferred from noisy or misclassified data. The msm package includes functions for model fitting, likelihood calculation, and model comparison, allowing researchers to assess the fit of their models and make adjustments as needed. The package is widely used in medical research, particularly for chronic diseases, but also in other fields such as geology, zoology, and econometrics. It provides tools for estimating transition intensities, calculating confidence intervals, and assessing model performance through diagnostic plots and formal goodness-of-fit tests. The package is flexible, allowing for the inclusion of covariates and the modeling of complex transition patterns. However, it has limitations in handling more complex models that require continuous-time observations or non-parametric baseline hazards, which are better addressed by other packages like mstate. Overall, the msm package is a powerful tool for analyzing panel data with multi-state models, offering a balance between flexibility and computational efficiency.The msm package in R provides a flexible and comprehensive tool for fitting Markov multi-state models to panel data. These models are used to analyze the progression of diseases or other processes over time, where individuals move between different states. The package allows for the inclusion of covariates, time-varying intensities, and hidden Markov models for misclassified or noisy data. It is designed to be user-friendly, with detailed documentation and support for various types of data, including chronic disease progression and screening. The package supports both time-homogeneous and time-inhomogeneous models, where transition intensities may vary over time. It also handles censored states, where the exact state at the end of the study is unknown. Hidden Markov models are used when the true states are not directly observed but inferred from noisy or misclassified data. The msm package includes functions for model fitting, likelihood calculation, and model comparison, allowing researchers to assess the fit of their models and make adjustments as needed. The package is widely used in medical research, particularly for chronic diseases, but also in other fields such as geology, zoology, and econometrics. It provides tools for estimating transition intensities, calculating confidence intervals, and assessing model performance through diagnostic plots and formal goodness-of-fit tests. The package is flexible, allowing for the inclusion of covariates and the modeling of complex transition patterns. However, it has limitations in handling more complex models that require continuous-time observations or non-parametric baseline hazards, which are better addressed by other packages like mstate. Overall, the msm package is a powerful tool for analyzing panel data with multi-state models, offering a balance between flexibility and computational efficiency.